Detecting and recognizing human faces automatically in digital images strongly enhance content-based video indexing systems. In this paper, a novel scheme for human faces detection in color images under nonconstrained scene conditions, such as the presence of a complex background and uncontrolled illumination, is presented. Color clustering and filtering using approximations of the YCbCr and HSV skin color subspaces are applied on the original image, providing quantized skin color regions. A merging stage is then iteratively performed on the set of homogeneous skin color regions in the color quantized image, in order to provide a set of potential face areas. Constraints related to shape and size of faces are applied, and face intensity texture is analyzed by performing a wavelet packet decomposition on each face area candidate in order to detect human faces. The wavelet coefficients of the band filtered images characterize the face texture and a set of simple statistical deviations is ...

It is evident that the process of face recognition, by definition, should be based on the content of a face. The problem is: what is a "face"? Recently, a state-of-the-art statistics-based face recognition system, the PCA plus LDA approach, has been proposed [1]. However, the authors used "face" images that included hair, shoulders, face and background. Our intuition tells us that only a recognition process based on a "pure" face portion can be called face recognition. The mixture of irrelevant data may result in an incorrect set of decision boundaries. In this paper, we propose a statistics-based technique to quantitatively prove our assertion. For the purpose of evaluating how the different portions of a face image will influence the recognition results, a hypothesis testing model is proposed. We then implement the above mentioned face ...

An omni-face detection scheme for image/video content description is proposed in this paper. It provides the ability to extract high-level features in terms of human activities rather than low-level features like color, texture and shape. The system relies on an omni-face detection system capable of locating human faces over a broad range of views in color images or videos with complex scenes. It uses the presence of skin-tone pixels coupled with shape, edge pattern and face-specific features to locate faces. The main distinguishing contribution of this work is being able to detect faces irrespective of their poses, including frontal-view and side-view, whereas contemporary systems deal with frontal-view faces only. The other novel aspects of the work lie in its iterative candidate filtering to segment objects from extraneous region, the use of Hausdorff distance-based normalized similarity measure to identify side-view facial profiles, and the exploration of hidden Markov model (HMM) ...

In this paper, a coarse-to-fine, LDA-based face recognition system is proposed. Through careful implementation, we found that the databases adopted by two state-of-the-art face recognition systems [4, 5] were incorrect because they mistakenly use some none-face portions for face recognition. Hence, a face-only database is used in the proposed system. Since the facial organs on a human face only differ slightly from person to person, the decision-boundary determination process is tougher in this system than it is in conventional approaches. Therefore, in order to avoid the above mentioned ambiguity problem, we propose to retrieve a closest subset of database samples instead of retrieving a single sample. The proposed face recognition system has several advantages. First, the system is able to deal with a very large database and can thus provide a basis for efficient search. Second, due to its design nature, the system can handle the defocus and 1 This work was partially sup...

We propose to employ near-infrared (NIR) images for face recognition in reduced illumination or total darkness. A homomorphic processing technique has been developed to effectively reduce the artifact of NIR images [1]. In this paper, we proceed to construct a facial feature detection system that would function independent of the surrounding lighting condition. Firstly, we propose a classification method based on local histogram analysis to separate NIR images captured at a short range from those in other circumstances. Afterwards, we present an algorithm to mark predominant facial features in a nearly frontal-face NIR images acquired at a short range. Experimental results demonstrate that facial feature points can be located accurately in homomorphic-filtered NIR images. 1.

This paper presents a fast and efficient head tracking approach. Skin detection, gray-scale morphology and a geometrical face model are applied to detect roughly the face region and extract facial features automatically. A novel Kalman filtering framework is utilized for tracking and estimation of the 3-D pose of the moving head. An application to cursor control on a computer display is presented. Experiments with real image sequences show that the system is able to extract and track facial features reliably. Pose estimation accuracy was tested with synthetic data and good preliminary results were obtained. The real-time performance achieved indicates that the proposed system can be applied in platforms where computational resources are limited. 1.

In this paper we suggest a novel method which is robust and efficient in extracting eye windows using Wavelets and Neural Networks. Wavelet analysis is used as a pre-processor for a back propagation neural network with conjugate gradient learning. The inputs to the neural network are the wavelet maxima neighborhood coefficients of face images at a particular scale. The output of the neural network is the classification of the input into an eye or non-eye region. An accuracy of 88 % is observed for test images under different environment conditions not included during training. 1.

We present a technique for robustly and automatically detect a set of user-selected facial features in images, like the eye pupils, the tip of the nose, the mouth centre, etc. Based on a specific architecture of heterogeneous neural layers, the proposed system automatically synthesises simple problem-specific feature extractors and classifiers from a training set of faces with annotated facial features. After training, the facial feature detection system acts like a pipeline of simple filters that treats the raw input face image as a whole and builds global facial feature maps, where facial feature positions can easily be retrieved by a simple search for global maxima. We experimentally show that our method is very robust to lighting and pose variations as well as noise and partial occlusions. 1.

Face recognition, by definition, should be a recognition process in which recognition is based on the content of a face. The problem is: what is a "face"? Goudail et al. [1] and Swets and Weng [2] have recently proposed state-of-the-art statistics-based face recognition systems. However, they used "face" images that included hair, shoulders, face and background. Our intuition tells us that only a recognition process based on a "pure" face portion can be called face recognition. The mixture of irrelevant data may result in an incorrect set of decision boundaries. In this paper, we propose a statisticsbased technique to quantitatively prove our assertion. For the purpose of evaluating how the different portions of a face image will influence the recognition results, two hypothesis testing models are proposed. We then implement the two above mentio...

Automatic face detection in digital video is becoming a very important research topic, due to its wide range of applications, such as security access control, model-based video coding or content-based video indexing. In this paper, we present a connectionist approach for detecting and precisely localizing semi-frontal human faces in complex images, making no assumption on the content or the lighting conditions of the scene, neither on the size, the orientation, and the appearance of the faces. Unlike other systems depending on a hand-crafted feature detection stage, followed by a feature classification stage, we propose a convolutional neural network architecture designed to recognize strongly variable face patterns directly from pixel images with no preprocessing, by automatically synthesizing its own set of feature extractors from a large training set of faces. Moreover, the use of receptive fields, shared weights and spatial subsampling in such a neural model provides some degrees of invariance to translation, rotation, scale, and deformation of the face patterns. We present in details the optimized design of our architecture and our learning strategy. Then, we present the process of face detection using this architecture. Finally, we provide experimental results to demonstrate the robustness of our approach and its capability to precisely detect extremely variable faces in uncontrolled environment.